Object digitization

ABSTRACT

Digitizing objects in a picture is discussed herein. A user presents the object to a camera, which captures the image comprising color and depth data for the front and back of the object. For both front and back images, the closest point to the camera is determined by analyzing the depth data. From the closest points, edges of the object are found by noting large differences in depth data. The depth data is also used to construct point cloud constructions of the front and back of the object. Various techniques are applied to extrapolate edges, remove seams, extend color intelligently, filter noise, apply skeletal structure to the object, and optimize the digitization further. Eventually, a digital representation is presented to the user and potentially used in different applications (e.g., games, Web, etc.).

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority to the provisional applicationassigned U.S. Patent Ser. No. 61/493,844, which was filed on Jun. 6,2011 and entitled OBJECT DIGITIZATION.

BACKGROUND

Modern gaming and Internet technologies interact with users in far morepersonal ways than these technologies have in the past. Instead ofsimply hitting buttons on a controller connected to a game console,today's gaming systems can read movements of players standing in frontof cameras or actions players take with wireless controllers (e.g.,swinging a controller like a baseball bat). This personal interactionopens up an entire new realm of gaming.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter. Nor is this summaryintended to be used as an aid in determining the scope of the claimedsubject matter.

One aspect is directed to creating a digital representation (a“digitization”) of an object in an image. A user presents the object toa camera, which captures the image comprising color and depth data forthe front and back of the object. For both front and back images, theclosest point to the camera is determined by analyzing the depth data.From the closest points, edges of the object are found by noting largedifferences in depth data. The depth data is also used to constructpoint cloud constructions of the front and back of the object. Varioustechniques are applied to extrapolate edges, remove seams, extend colorintelligently, filter noise, apply skeletal structure to the object, andoptimize the digitization further. Eventually, a digital representationis presented to the user and potentially used in different applications(e.g., games, Web, etc.).

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Illustrative embodiments of the present invention are described indetail below with reference to the attached drawing figures, andwherein:

FIG. 1 is a block diagram of an exemplary computing environment suitablefor implementing embodiments discussed herein.

FIG. 2 is a diagram of a user presenting an object for digitization,according to one embodiment.

FIG. 3 is a diagram of a work flow for digitizing an object, accordingto one embodiment.

FIGS. 4A and 4B are diagrams of camera-view perspectives of a userpresenting an object for digitization, according to one embodiment.

FIG. 5 is a diagram of a segmented depth image usable to digitize anobject, according to one embodiment.

FIG. 6 is a diagram of depth-to-color offsets, according to oneembodiment

FIG. 7 is a diagram of a source color image usable to digitize anobject, according to one embodiment.

FIG. 8 is a diagram of a color segmentation of a captured object,according to one embodiment.

FIGS. 9 and 10 are diagrams of user interfaces (UIs) giving guidance forholding objects to be digitized, according to one embodiment.

FIG. 11 is a diagram of a three-dimensional (3D) point cloudconstruction of an object, according to one embodiment.

FIG. 12 is a diagram of two views of aligned point sheets, according toone embodiment.

FIG. 13 is a diagram of a final point cloud construction, according toone embodiment.

FIG. 14 is a diagram of a UI displaying a confirmation image of adigitized object displayed to a user, according to one embodiment.

FIG. 15 is a diagram of a mesh output of a captured image, according toone embodiment.

FIG. 16 is a diagram of a smoothed and processed image of an object,according to one embodiment.

FIG. 17 is a diagram of an image with UV coordinates, according to oneembodiment.

FIG. 18 is a diagram of front-facing triangle edges drawn into a sectionof a final texture map, according to one embodiment.

FIGS. 19A-19E are diagrams illustrating weighting added to the differentbones of a generated skeletal structure, according to one embodiment.

FIGS. 20A and 20B are diagrams illustrating before and after luma/chromaprocessing, according to one embodiment.

FIGS. 21A and 21B are diagrams illustrating source and output imagesafter edges are filtered, according to one embodiment.

FIGS. 22A and 22B are diagrams illustrating images where the edge repairfilter finds background colors and target object colors, according toone embodiment.

FIGS. 23A and 23B is a diagram of images showing distance from an edgeto a disputed region and calculated background likelihood values,according to one embodiment.

FIG. 24 is a diagram of a final composite texture map, according to oneembodiment.

FIGS. 25A and 25B is a diagram of masked values and heavily blurredvertex colors, according to one embodiment.

FIGS. 26A and 26B is a diagram of different meshes with texture only andtexture with vertex color blending by mask value, according to oneembodiment.

FIG. 27 is a diagram of a final rendering of the digitized object,according to one embodiment.

FIG. 28 shows a flow chart detailing a work flow for digitizing anobject, according to one embodiment.

FIG. 29 shows a flow chart detailing a work flow for digitizing anobject, according to one embodiment.

DETAILED DESCRIPTION

The subject matter of embodiments of the present invention is describedwith specificity herein to meet statutory requirements. But thedescription itself is not intended to necessarily limit the scope ofclaims. Rather, the claimed subject matter might be embodied in otherways to include different steps or combinations of steps similar to theones described in this document, in conjunction with other present orfuture technologies. Terms should not be interpreted as implying anyparticular order among or between various steps herein disclosed unlessand except when the order of individual steps is explicitly described.

Embodiments described herein generally relate to creating a digitalrepresentation of an object captured by a camera. In one embodiment, auser holds the object in front of the camera, the camera captures animage of the object, and a device digitizes the captured object into a3D rendition that can be displayed digitally—for instance, as an entityin a video game. To illustrate, consider the following example. A userholds up a toy octopus to a gaming device equipped with a camera. Usingthe camera, the gaming device takes pictures of the front and back ofthe object, capturing both color and depth data for each side. Based onthe depth data, a 3D rendition of the octopus is constructed, and thecolor data is then added to the 3D rendition to create a digitalrendition (referred to herein as a “digitization”) of the octopus. Thedigitization can then be used in games or any other software or webapplication where display of the octopus is useful.

At least one embodiment is directed towards digitizing an object. A userpresents the object to a camera on a computing device (such as a gamingconsole). The device may instruct the user to position the object fordisplay to optimize captured images—e.g., by placing an outline on ascreen reflecting the image being seen by the camera and indicating thatthe user should move the object into the outline. Eventually, the devicecaptures an image, or images, of the object. The user may then beinstructed to present the backside of the object to the camera forcapturing. The device may then capture an image, or images, of thebackside of the object. The captured front and back images are processedto construct a 3D digitization of the object.

In one embodiment, processing uses depth data of the images captured bythe camera. Depth data describes the proximity of things captured in theimages in a per-pixel or other spatial representation. Using the depthdata, the closest point of an object in the image is located. Thisembodiment assumes that the closest object an image is the object theuser is looking to capture—e.g., a user holding an octopus to camerawould likely mean that the octopus is the closest thing to the camera.

Having briefly described in an overview of the present invention, anexemplary operating environment in which various aspects of the presentinvention may be implemented is now described. Referring to the drawingsin general, and initially to FIG. 1 in particular, an exemplaryoperating environment for implementing embodiments of the presentinvention is shown and designated generally as computing device 100.Computing device 100 is but one example of a suitable computingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of the invention. Neither should thecomputing device 100 be interpreted as having any dependency orrequirement relating to any one or combination of componentsillustrated.

Embodiments of the invention may be described in the general context ofcomputer code or machine-useable instructions, includingcomputer-executable instructions such as program modules, being executedby a computer or other machine, such as a personal data assistant orother handheld device. Generally, program modules including routines,programs, objects, components, data structures, etc., refer to code thatperform particular tasks or implement particular abstract data types.Embodiments of the invention may be practiced in a variety of systemconfigurations, including hand-held devices, consumer electronics,general-purpose computers, more specialty computing devices, and thelike. Embodiments of the invention may also be practiced in distributedcomputing environments where tasks may be performed by remote-processingdevices that may be linked through a communications network.

With continued reference to FIG. 1, computing device 100 includes a bus101 that directly or indirectly couples the following devices: memory102, one or more processors 103, one or more presentation components104, input/output (I/O) ports 105, I/O components 106, and anillustrative power supply 107. Bus 101 represents what may be one ormore busses (such as an address bus, data bus, or combination thereof).Although the various blocks of FIG. 1 are shown with lines for the sakeof clarity, in reality, delineating various components is not so clear,and metaphorically, the lines would more accurately be grey and fuzzy.For example, one may consider a presentation component such as a displaydevice to be an I/O component. Additionally, many processors havememory. The inventors hereof recognize that such is the nature of theart, and reiterates that the diagram of FIG. 1 is merely illustrative ofan exemplary computing device that can be used in connection with one ormore embodiments of the present invention. Distinction is not madebetween such categories as “workstation,” “server,” “laptop,” “gamingconsole,” “hand-held device,” etc., as all are contemplated within thescope of FIG. 1 and reference to “computing device.”

Computing device 100 typically includes a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by computing device 100 and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable media may comprisecomputer-storage media and communication media. Computer-storage mediaincludes both volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage of informationsuch as computer-readable instructions, data structures, program modulesor other data. Computer-storage media includes, but is not limited to,Random Access Memory (RAM), Read Only Memory (ROM), ElectronicallyErasable Programmable Read Only Memory (EEPROM), flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otherholographic memory, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to encode desired information and which can be accessed by thecomputing device 100.

The memory 102 includes computer-storage media in the form of volatileand/or nonvolatile memory. The memory 102 may be removable,non-removable, or a combination thereof. Exemplary hardware devicesinclude solid-state memory, hard drives, optical-disc drives, etc. Thecomputing device 100 includes one or more processors that read data fromvarious entities such as the memory 102 or the I/O components 106. Thepresentation component(s) 104 present data indications to a user orother device. Exemplary presentation components include a displaydevice, speaker, printing component, vibrating component, and the like.

The I/O components 106 may comprise a camera capable of taking staticpictures or video. In one embodiment, the camera, when taking a picture,captures color data (e.g., red, green, blue) and depth data. Depth dataindicates the proximity—in one embodiment, on a per-pixel basis—ofobjects being captured by the camera to the camera itself. Depth datamay be captured in a number of ways, like using an infrared (IR) camerato read projected IR light, reading projected laser light, or the like.Depth data may be stored in a per-centimeter, per-meter, or otherspatial representation. For example, IR dots may be projected and readby an IR camera, producing an output file that details the depth of animage in an area directly in front of the camera, measured in aper-meter orientation. Additionally, depth data may also indicate theorientation of a particular part of a captured object by recording thepixels of screen area where depth is measured. Because the color cameraand the depth camera may be located separately from one another,conversions may be made to map retrieved color data to correspondingdepth data.

The I/O ports 118 allow the computing device 100 to be logically coupledto other devices including the I/O components 120, some of which may bebuilt in. Illustrative I/O components 120 include a microphone,joystick, game pad, satellite dish, scanner, printer, wireless device,and the like.

As indicated previously, some embodiments are directed to creating adigital rendition of an object in a virtual environment. FIG. 2 is adiagram of an environment 200 for a user 204 to create a digitalrepresentation of an object 206, according to one embodiment. It shouldbe understood that this and other arrangements described herein are setforth only as examples. Other arrangements and elements (e.g., machines,interfaces, functions, orders, and groupings of functions, etc.) can beused in addition to or instead of those shown, and some elements may beomitted altogether. Further, many of the elements described herein arefunctional entities that may be implemented as discrete or distributedcomponents or in conjunction with other components, and in any suitablecombination and location. Various functions described herein as beingperformed by one or more entities may be carried out by hardware,firmware, and/or software. For instance, various functions may becarried out by a processor executing instructions stored in memory.

Focusing on FIG. 2, environment 200 shows user 204 presenting the object206, illustrated as octopus figurine, to a computing device 202, whichis equipped with two cameras: color camera 208 and depth camera 210. Inenvironment 200, computing device 202 is a game console, such as theMicrosoft Kinect™ created by the Microsoft Corporation®. The cameras oncomputing device 202 capture one or more images that include the object206. Color camera 208 captures color data for images, and depth camera210 captures depth data. In alternative embodiments, computing device202 may only have one camera that captures both color and depth data.

While shown as a standalone device, computing device 202 may beintegrated or communicatively connected to other computing devices(e.g., gaming consoles, servers, etc.). The components of the computingsystem 200 may communicate with each other via a network, which mayinclude, without limitation, one or more local area networks (LANs)and/or wide area networks (WANs). Such networking environments arecommonplace in offices, enterprise-wide computer networks, intranets andthe Internet. It should be understood that some embodiments may includeadditional computing devices 202. Each may comprise a singledevice/interface or multiple devices/interfaces cooperating in adistributed environment.

In some embodiments, one or more of the digitization techniquesdescribed herein may be implemented by stand-alone applications.Alternatively, one or more of the digitization techniques may beimplemented by disparate computing devices across a network, such as athe Internet, or by a module inside a gaming system. It will beunderstood by those of ordinary skill in the art that thecomponents/modules illustrated in FIG. 2 are exemplary in nature and innumber and should not be construed as limiting. Any number ofcomponents/modules may be employed to achieve the desired functionalitywithin the scope of embodiments hereof. Further, components/modules maybe located on any number of servers or client computing devices.

While user 204 is shown in FIG. 2 as presenting the front-side of object206 to computing device 202, user 204 may present the backside of theobject 206 to computing device 202 so a backside image of object 206 canbe captured. The backside image can then be combined with a front sideimage of object 206 to produce a 3D rendition of object 206. Eachcaptured image may include color and depth data, both of which allowcomputing device 202 to accurately create a 3D rendition of object 106.

Additional image views of object 106 may also be used, in differentembodiments, to aid digitization. Object 106 may be photographed orvideoed from any different angle. For example, several images may betaken from the right, left, bottom, and top of image 106 in additionto—or in lieu of—front and back views. in order to generate a morerobust 3D digitization. For example, several side views may be used indigitizing a particular side of object 106. At least in embodiment, themore the views of object 106 used, the more complete or accurate a 3Drendition.

FIG. 3 is a diagram of a work flow 300 for digitizing an object,according to one embodiment. Initially, a user presents the object to acamera on a computing device to images taken, as shown at 302. Thecomputing device may, in some embodiments, instruct the user to move theobject into a specific area in order to capture an optimal image of theimage—for example, asking providing an outline on a display, showing areal-time image of the user and the object, and then instructing theuser to move the object into the outline. Once an initial image istaken, the computing device may instruct the user to present thebackside of the object for capturing, as shown at 304. Guidance forcapturing the backside may similarly be provided by the computingdevice. For each image captured, color and depth data are stored andused to digitize the object being presented. Moreover, multiple imagesmay be captured for the front and backside perspectives of the object.For example, the computing device may be configured to take ten frontimages and ten back images, and possibly merge the front ten togetherand the back ten together—or use all twenty to digitize the image. Whileten images have shown to be an ideal number of images to digitize anobject, other embodiments may use different numbers of captured images.

Once front and back images of the object are captured by the camera, oneembodiment begins digitizing the object by searching—using depth data ofthe images—for the closest point in the image to the camera, as shown at306. The user is probably holding the object to be digitized in front ofthe user, so it the object should be closer to the camera than anythingelse. Turning back to FIG. 2 for a second, one may notice that user 204is holding the object 206 in front of him and thus closer to thecomputing device 202. Locating the closest object in the image may beaccomplished using the depth data associated with the image, and someembodiments perform the process on both front and backside images toidentify the closest object in both.

As indicated at 308, the closest objects identified in the images arethen searched for edges to identify where the objects end. Depth data isagain used to locate the edges of objects in the image. Edge searchingmay commence outwardly from the closest point, looking for drasticdifferences in the depths of points. For example, the edge of theoctopus in FIG. 2 may have a point that is nearly half a meter closerthan an adjacent point representing user 204's shoulder. Such a drasticdifference represents a readable signal that the adjacent point is notpart of the object and thus should not be included in furtherdigitization steps. Locating all the edges of an object in such a mannerallows the computing device to identify the object in the image.

Once the object is determined, one embodiment switches off the colordata associated with the rest of the image (i.e., the portion of theimage not identified as the object). It may be necessary in someembodiments to capture multiple images (e.g., ten images of the frontand ten of the back of the object), so a smoothing technique may berequired to blend the found edges between frames, as shown at 310. Forexample, the object may have moved between frame one and frame four sosmoothing the edges between the frames may be necessary to get anaccurate representation of the object. Additionally, noise, lowresolution, and imperfections in depth-to-color registration may alsonecessitate additional smoothing and/or filtering of the edges.

In one embodiment, the resultant smoothed and/or filtered object ispresented to the user for confirmation, as shown at 312. The user canthen accept or reject the resultant object. If accepted, additionalprocessing may then proceed to digitize the object. If rejected,embodiments may ask the user to begin the process over by taking newpictures of the object, or may simply re-smooth or re-filter the object.

Eventually, the front and back images are used to generate a point cloudconstruction of the object in 3D. A “point cloud construction,” shown indetail in FIG. 11 is a mapping of the front and/or back images of theobject into 3D space, with the depth of each point or pixel of theobject identified. The point cloud construction may be used in furtherdigitization of the object. Although, alternative embodiments may useother representations or spatial aggregates of depth and color data tocreate constructions or other types of representations of the objectfrom different images.

FIGS. 4-26 show images of various steps in the digitization process andwill be discussed in further detail below to illustrate the processingused by different embodiments. Specifically, FIGS. 4A and 4B arediagrams of camera-view perspectives of a user presenting an object fordigitization, according to one embodiment. In the illustratedembodiment, two views of the object are captured. The color camera iszoomed in on the center of the frame to get a 640×480 color windowaround the target object, and the corners of the color window are thentransformed into depth frame coordinates (assuming the corners are atthe front of the target object). A matching 160×120 window is thengrabbed from the depth frame. Without this per-frame window adjustment(dependent on the distance of the target object to the camera), thedepth and color windows may not overlap as fully as possible. Moreover,raw color and depth may be captured without performing depth-to-color orcolor-to-depth registration. The resolution numbers and windows aremerely provided for illustrative purposes, as various other resolutionsmay alternatively be used.

In one embodiment, the depth image is segmented to the target object. Todo so, the closest depth pixel to the camera is searched for and found,assuming that such a point is on the target object. This embodiment thenflood-fills outwardly from found closest point until depth edges (i.e.,where depth is too far from the front of the object or we have no depthdata) are hit. Additionally, points around high slope areas and with toofew neighbors may be removed. The result is a mask of depth pixels thatare on the target object (referred to herein as a “segmented depthimage”), as shown in FIG. 5. The segmented depth image is stored in aring buffer of depth frames (BAB/GOE shipped with a ring buffer size of10), overwriting the oldest depth frame and averaging all framestogether to get a final depth image. In one embodiment, only segmenteddepth pixels contribute to the final average. As a result, noise issmoothed, resulting in a more stable object edge and improving scenarioswhere parts of the object are blinking in and out of the segmentationdue to noise or poor IR reflecting materials.

FIG. 6 is a diagram of depth-to-color offsets, according to oneembodiment. As illustrated, one embodiment builds a depth-to-coloroffset table with the green colors (shown in the uppermost, rightcorner), red colors (shown in the lower left corner), and blending ofthe two in between. The offsets between each pixel's depth and colorspace coordinates are stored in a table for rapid lookup during colorsegmentation and mesh processing, as well as to aid perfectlyreproducing output meshes using only the two captured color images,regardless of the calibration settings of a particular camera. Regionsof the table outside the object segmentation may be filled in by copyingoffsets at the edge of the segmentation outwards. The copied offset atthe edge may be used later to handle cases when vertices in the outputmesh projected into the depth image fall outside the bounds of the depthsegmentation.

FIG. 7 is a diagram of a source color image, and FIG. 8 is a diagram ofa color segmentation of a captured object, according to one embodiment.Starting with the segmentation in depth space, one embodiment splatseach segmented depth pixel into a 320×240 color segmentation buffer,using a star-like splat pattern. The resultant pattern may then be“up-sampled” to 640×480, and a “distance-from-ideal” value, whichdescribes how far the source depth pixel is from the “ideal” distance,is then computed for each segmented color pixel. The ideal distancerepresents how close to the camera the user should hold the targetobject in order to get as much color/depth data as possible—withoutintersecting the front clip plane of the depth camera. These values maybe presented as feedback to the user during the capture process. Pixelsfurther from ideal may be blurred and tinted more heavily than pixelscloser to ideal. The distance-from-ideal values are eventually copiedinto the alpha channel of the color image used for real-time previewing.

FIGS. 9 and 10 are diagrams of user interfaces (UIs) giving guidance forholding objects to be digitized, according to one embodiment. FIG. 9shows that the illustrated embodiment analyzes the number of segmentedpixels, distance to the camera, distance from the center of the cameraview, pixel stability, and object size, and gives the user visual andtext feedback on how to best position the object. The feedback may be inthe form of an outline on a screen. FIG. 10 shows color and depth dataof an image of the back of the target object, using the same process asabove. One embodiment guides the user to orient the object correctlyusing the outline of the segmented front capture. The user may not haveto match the outline precisely because front and back captures may laterbe aligned automatically.

FIG. 11 shows a point cloud construction, according to one embodiment.At this point two color and depth data images have been segmented to thetarget object. Using these images, a point cloud construction of pointson the surface of the target object can be built and later used toreconstruct a triangle mesh. Segmented pixels in the front depth imageare transformed into a “sheet” of 3D points. In one embodiment,positions are un-projected from depth image space into model space usingdepth data and the origin being the back-center of the sheet. The edgesof the sheet are extruded backwards by adding additional points to formthe sides of the object. To guess how “deep” the object is, in BAB/GOE,a fixed value for the extrude distance may be used.

Similarly, a sheet of 3D points from the back depth image is created,using the back-center of the front capture as the origin. FIG. 12illustrates two views of aligned point sheets, according to oneembodiment. To align the sheets, an initial transform is calculated torotate this sheet 180 degrees around the up axis so that it forms theback of the point cloud. In one embodiment, another transform iscalculated that aligns the edges of the front and back sheets as closelyas possible. The alignment process may translate the back sheet to matchthe center of mass of the back sheet with center of mass of the frontsheet. A brute-force iterate is then used over a range of translationsand rotations to minimize an “alignment error” value, computed as thesum of the distances from each front edge point to its closest back edgepoint. The iterate may be done in multiple passes (with each passattempting to compute the best value for each translation and rotationaxis one at a time), and the search across each axis is done using atwo-tier hierarchical approach for efficiency. Closest-point-finding isaccelerated using a 3D cell space partition. One embodiment alsoimplements an iterative closest point (“ICP”) algorithm for fastfine-grained alignment, or alternatively, the need for better controlmay dictate use of only the brute-force method iterative.

Points from the front sheet that do not have corresponding points in theback sheet may be culled to search backwards from each front point tofind the nearest back point. Likewise, points from the back sheet thatdo not have corresponding points in the front sheet may be culled. Thisremoves parts of the sheet that are inconsistent between the front andback captures, as can happen if the user's hand is in the capture buthas changed position between captures, or if the object has changedshape between front and back captures.

In one embodiment, the remaining points are merged together into a finalpoint cloud, and the normals for the points are computed using the planeformed by each point and its right and lower neighbors. FIG. 13 shows afinal point cloud construction, according to one embodiment.

A confirmation image may then be presented to the user, as shown in FIG.14. The confirmation image incorporate the results of sheet alignmentand point culling, allowing the user to detect cases when capture,alignment, or culling have failed badly and to re-capture without havingto go through the remainder of the construction process. The image iscreated by projecting and splatting points in the final point cloud intothe alpha channel of the front and back color images, rotating the backimage based on the alignment transform, and doing some additional imagecleanup.

A surface reconstruction step takes the final point cloud and generatesa triangle mesh. FIG. 15 illustrates a diagram of a mesh output withsurface reconstruction. One embodiment uses a hybrid CPU/GPUimplementation of the Poisson Surface Reconstruction algorithm developedby Minmin Gong in Xin Tong's group at MSR-Beijing and detailed in“Poisson Surface Reconstruction,” by Kazhdan, Bolitho, and Hoppe; and“Highly Parallel Surface Reconstruction” by Zhou, Gong, Huang, and Guo.This may be the most computationally intense part of digitization inboth memory and time, taking, in some embodiments, 10-20 seconds for atypical point cloud data of approximately 20,000 points. The amount ofhole-filling may be limited during reconstruction to keep memory usageunder control, but such limiting can result in non-water-tight meshes ifthere are large holes in the point cloud.

FIG. 16 is a diagram of a smoothed and processed image of an object,according to one embodiment. Vertex adjacency lists are built and faceand vertex normals are computed. Then, one embodiment uses a Laplacianalgorithm to smooth some constraints. As a result, the sides of theobject are rounded off, noise removed, and areas where the point sheetsdo not line up perfectly are cleaned up.

Depending on the quality of the point cloud, the surface reconstructioncan create small “islands” of geometry instead of a single large mesh.One embodiment uses connected component labeling to find islands,compute their volumes, and remove islands that are significantly smallerthan the largest island.

For each vertex, one embodiment looks at the dot product between thatvertex's normal and the front and back capture view directions. Thefront view direction may be along the model-space negative Z axis, whilethe back view direction may depend on the results of the sheet alignmentprocess and not along the positive Z axis. As a result, some verticesmay be visible to both the front and back capture views, and somevertices may be visible to neither view. Some vertices may be classifiedas “front” if their normal is facing the front more than the back andvice versa. This also allows for location of the “seam” vertices (i.e.the vertices that straddle the front and back views of the object).

To create the texture map to apply onto the final mesh, one embodimentplaces a color image from the front capture at the top of the textureand the color image from the back capture directly under the frontcapture. Texels from the top part of the texture are then mapped ontothe primarily front-facing triangles and vice versa for the primarilyback-facing triangles. Vertices may initially be shared between frontand back triangles right along the front-back seam, and later, theseshared vertices may be duplicated so that to map different parts of thetexture to front versus back triangles.

FIG. 17 illustrates a diagram of an image with UV coordinates, and FIG.18 illustrates a diagram of front-facing triangle edges drawn into asection of a final texture map, according to one embodiment. To computeUV coordinate, front-facing triangles are mapped to the top part of thetexture where placed the front capture color image is placed, andlikewise for the bottom. Vertex positions are in the space of the depthcamera; whereas, the color images are in the space of the color camera,so after projecting vertices onto the front/back depth images, oneembodiment uses the depth-to-color offset table to transform coordinatesinto the color camera space.

In one embodiment, the mesh is re-centered, mirrored about the up axis,and scaled to enforce a maximum width/height aspect ratio. The capturedcolor and depth images are mirrored compared to the real physicalobject, so another mirroring is used to reverse this. A skeleton may beoptimized and animations may be added for taller rather than widerobjects, so the width/height aspect ratio restriction puts a bound onartifacts caused by animating wide objects that do not match a certainskeleton.

In one embodiment, a single skeleton is used for all animations theskeleton. The skeleton may have bones to give a good range of motions(walking, jumping, crawling, dancing, looking left and right, etc.)without requiring the target object to have much more shape.

To apply skin to the digitized image, the mesh is rescaled andpositioned such that skeleton fits inside of it, with the top bonepositioned a certain percentage (e.g., approximately 90%) from the topof the object (placing it roughly inside the “head” of the object) andthe bottom bone at the bottom extent of the object. Bone indices canthen be computed and weights added to the skeleton by finding theclosest bones along the up axis to each vertex and weighting to themusing a falloff curve. FIGS. 19A-19E are diagrams illustrating weightingadded to the different bones of a generated skeletal structure,according to one embodiment.

Color and/or depth images are processed to reduce noise and improvequality. Processing is done on the front and back images independently,in one embodiment, and the results are merged into a final texture map,which may require additional processing. After some experimentation andfeedback from artists, the following steps were found to be optimal:convert sRGB colors to linear space, apply “grey world” auto-whitebalance, repair edge artifacts, compute luma and chroma values, applybilateral filtering, histogram equalization, and sharpening to luma,apply median filtering to chroma, convert back to sRGB, and finally,extend the edges of the colors outwards into the de-segmented regions ofthe image. Other steps may be added and some of the above deleted indifferent embodiments.

FIGS. 20A and 20B show images before and after luma/chroma processing,according to one embodiment.Processing luma/chroma independently allowsfor filtering chroma much more strongly while preserving details in theluma image, which is good for de-noising the image. Histogramequalization may be applied very lightly to compensate for poorlyexposed images.

FIGS. 21A and 21B show source and output images after edges arefiltered, according to one embodiment. In one embodiment, an “edgerepair filter” attempts to replace colors at the edges of the targetobject that are actually from the background and not the object itself.Bad colors may creep in due to the relatively low resolution and highnoise of the depth image and imperfect depth-to-color registration. Theedge repair filter operates on a “disputed region” of pixels directlyaround the object edge. Using the assumption that pixels interior to thedisputed region are definitely part of the target object and pixelsfurther exterior are part of the background, a “background likelihood”value is computed per disputed region pixel and used to blendhigh-likelihood-background pixels towards interior colors.

FIGS. 22A and 22B show images where the edge repair filter findsbackground colors and target colors, according to one embodiment. Thetarget colors are extrapolated into a disputed region from the outside.

FIGS. 23A and 23B are images showing distance from an edge to a disputedregion and calculated background likelihood values, according to oneembodiment. Furthermore, FIG. 24 shows a final composite texture map ofthe image with texturing over tope of a non-finalized image, accordingto one embodiment.

Seams resulting from placing front and back images together may need tobe repaired. The last bit of mesh processing is used to improve theappearance of the object near the front-back seam and in regions thatwere invisible to the color camera during capturing. First, a mask valueper vertex is computed that represents how “bad” the texture color willbe at that vertex. This value is the product of distance to the seam(where the front and back images touch but do not generally line upwell) and how back-facing a vertex is to any of the captured images(where texture colors break down due to the surface facing away from thecamera views and also from poor texel density). These values may bestored in an vertex color alpha channel. Next, a blurred version of thesurface color is computed and stored into the vertex color RGB channels.These colors are fairly good in quality (although low in detail). Thenegative artifacts needing repair are relatively localized and of ahigher frequency, where-as the blurring gives more global, low-frequencycolors.

FIGS. 25A and 25B show masked values and heavily blurred vertex colors,according to one embodiment. At run-time, mask value is used to blendbetween the source texture and the blurred vertex color, in oneembodiment. FIGS. 26A and 26B show different meshes with texture only(26A) and texture with vertex color blending by mask value (26B),according to one embodiment.

FIG. 27 shows a final rendering of the digitized object, according toone embodiment. In one embodiment, once the final mesh and texture arecomplete, an Unreal Engine 3 mesh is created and rendered withenvironment and rim lighting, self-shadowing, and animation. The GOE appalso allows the user to digitize the object by mapping the Nui skeletononto the skeleton.

The above steps balance usability, CPU/GPU/memory constraints, outputquality, artistic concerns, sensor accuracy, and development time.Trade-offs were made that may not be specific to every scenario. Assuch, different steps could be added or some of the above deleted toimprove the speed or quality of the final digitization.

FIG. 28 shows a work flow 2800 for digitizing an object, according toone embodiment. Color and depth data for an image are received, as shownat 2802. Analyzing the depth data, an object of interest is found byidentifying the closest point of the image to the camera, based on theassumption that a user was most likely presenting the object to thecamera for capture. Alternative ways to determine the object of interestmay alternatively or additionally be used. Different image-recognitionor algorithmic matching techniques may be used to locate an object in animage, as embodiments are not limited to any specific type of means forlocating objects in images. Also, embodiments may use the color data ofthe image in addition or alternative to the depth data to locate anobject. For example, a Coca-Cola can may include a trademark color ofred, making color data particularly relevant when trying to locate thecan in a picture. Thus, the object of interest may be found in manydifferent ways.

Once the object of interest is located, the object's edges areidentified, as shown at 2806. Such a determination may be made byanalyzing color, depth, or contrast, differences in the image around theobject. Once the edges are located, a point cloud construction of theobject may be performed using the color and depth data of the image, asshown at 2808. To digitize the object in 3D, multiple point cloudconstructions for different sides of the object may be constructed basedon color and depth data of multiple images (e.g., back, front, top,bottom, etc.). Multiple point cloud constructions, once created, can beaggregated to create a final digitization of the object, as shown at2810.

FIG. 29 shows a work flow 2900 for digitizing an object, according toone embodiment. Once images of an object are received, as shown at 2902,the closest points of the image are identified, as shown at 2904. Sidesof an object (e.g., left, right, north, south, top, bottom, etc.) areidentified, as shown at 2906. Point cloud constructions of the imagesare created, as shown at 2908, and merged into a single rendition, asshown at 2910. The resultant rendition can then be saved, as shown at2912, and presented on a display device.

Many different arrangements of the various components depicted, as wellas components not shown, are possible without departing from the scopeof the claims below. Embodiments of our technology have been describedwith the intent to be illustrative rather than restrictive. Alternativeembodiments will become apparent to readers of this disclosure after andbecause of reading it. Alternative means of implementing theaforementioned can be completed without departing from the scope of theclaims below. Certain features and subcombinations are of utility andmay be employed without reference to other features and subcombinationsand are contemplated within the scope of the claims.

The invention claimed is:
 1. One or more computer storage devices havingcomputer-executable instructions embodied thereon that, when executed,digitize an object captured by at least one camera, the methodcomprising: receiving color and depth data associated with at least oneimage; identifying a closest point in the at least one image to the atleast one camera based on the depth data; identifying edges of theobject in the at least one image by analyzing depths of points movingoutwardly from the closest point looking for drastic differences indepths between adjacent points; generating a point cloud construction ofthe object using the depth data within the edges; and using the pointcloud construction to generate a digitization of the object.
 2. The oneor more computer storage devices of claim 1, wherein identifying theclosest point in the image to the at least one camera further comprises:calculating distances between an aperture of the at least one camera anda plurality of points in the image; and of the distances, selecting ashortest distance.
 3. The one or more computer storage devices of claimof claim 2, further comprising: identifying a feature connected to theobject at the shortest distance; and storing an indication of thefeature representing a closest portion of the object to the camera. 4.The one or more computer storage devices of claim 1, further comprising:receiving color and depth data for at least one second image of theobject; determining a second closest point in the at least one secondimage; identifying the object at the second closest point; andgenerating a second point cloud construction of the object, as orientedin the at least one second image.
 5. The one or more computer storagedevices of claim 4, combining the point cloud representation with thesecond point cloud construction to create a 3D rendition of the object.6. The one or more computer storage devices of claim 5, furthercomprising: identifying a seam between the point cloud construction andsecond point cloud construction; determining a filler color to fill aportion of the seam; filling the portion of the seam with the fillercolor to create the 3D rendition with a seamless edge between the pointcloud construction and the second point cloud construction; and storingthe 3D rendition.
 7. The one or more computer storage devices of claim6, further comprising displaying the 3D rendition on a display device.8. The one or more computer storage devices of claim 7, furthercomprising: using a set of rules to govern movement of differentfeatures of the 3D rendition; and moving the 3D rendition on the displaydevice according to one or more of the rules.
 9. The one or morecomputer storage devices of claim 1, wherein the method furthercomprises: performing an image analysis in a spatial region surroundingthe closest point in the at least one image; determining, based on theimage analysis, a color difference between two areas in the spatialregion; designating one of the areas as being associated with theobject; and removing another area with a different color than the one ofthe areas.
 10. The one or more computer storage devices of claim 1,further comprising flood-filling outwardly from the closest point untilone or more depth edges are reached.
 11. The one or more computerstorage devices of claim 1, further comprising removing one or morepoints of the at least one image around areas with fewer than athreshold number of neighbor points associated with the object,resulting in a mask of depth pixels of the object.
 12. The one or morecomputer storage devices of claim 11, further comprising storing themask of depth pixels in a ring buffer of depth frames in a manner thatoverwrites at least one depth frame and averages multiple framestogether to produce a final depth image.
 13. A method for digitizing anobject captured by at least one camera, the method comprising: receivingcolor and depth data associated with at least one image; identifying aclosest point in the at least one image to the at least one camera basedon the depth data; identifying edges of the object in the at least oneimage by analyzing depths of points moving outwardly from the closestpoint looking for drastic differences in depths between adjacent points;generating a point cloud construction of the object using the depth datawithin the edges; and using the point cloud construction to generate adigitization of the object.
 14. The method of claim 13, whereinidentifying the closest point in the image to the at least one camerafurther comprises: calculating distances between an aperture of the atleast one camera and a plurality of points in the image; of thedistances, selecting a shortest distance; identifying a featureconnected to the object at the shortest distance; and storing anindication of the feature representing a closest portion of the objectto the camera.
 15. The method of claim 13, further comprising: receivingcolor and depth data for at least one second image of the object;determining a second closest point in the at least one second image;identifying the object at the second closest point; generating a secondpoint cloud construction of the object, as oriented in the at least onesecond image; combining the point cloud representation with the secondpoint cloud construction to create a 3D rendition of the object;identifying a seam between the point cloud construction and second pointcloud construction; determining a filler color to fill a portion of theseam; filling the portion of the seam with the filler color to createthe 3D rendition with a seamless edge between the point cloudconstruction and the second point cloud construction; and storing the 3Drendition.
 16. The method of claim 15, further comprising: using a setof rules to govern movement of different features of the 3D rendition;and moving the 3D rendition according to one or more of the rules. 17.The method of claim 13, wherein the method further comprises: performingan image analysis in a spatial region surrounding the closest point inthe at least one image; determining, based on the image analysis, acolor difference between two areas in the spatial region; designatingone of the areas as being associated with the object; and removinganother area with a different color than the one of the areas.
 18. Acomputing device comprising: one or more processors configured to:receive color and depth data associated with at least one image;identify a closest point in the at least one image to the at least onecamera based on the depth data; identify edges of the object in the atleast one image by analyzing depths of points moving outwardly from theclosest point looking for drastic differences in depths between adjacentpoints; generate a point cloud construction of the object using thedepth data within the edges; and use the point cloud construction togenerate a digitization of the object.
 19. The one or more computerstorage devices of claim 1, wherein a drastic difference in depthsbetween adjacent points comprises at least a half a meter.
 20. The oneor more computer storage devices of claim 1, wherein a drasticdifference in depths between adjacent points corresponds to a distanceindicating that one of the adjacent points is not part of the capturedobject.